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Differentiation and classification of RNA motifs using small molecule-based pattern recognition.

Publication ,  Chapter
Padroni, G; Eubanks, CS; Hargrove, AE
January 2019

Understanding how to design small molecules that target coding and non-coding RNA has the potential to exponentially increase the number of therapeutically-relevant druggable targets, which are currently mostly proteins. However, there is limited information on the principles at the basis of RNA recognition. In this chapter, we describe a pattern-based technique that can be used for the simultaneous elucidation of RNA motifs and small molecule features for RNA selective recognition, termed Pattern Recognition of RNA by Small Molecules (PRRSM). We provide protocols for the computational design and synthetic preparation of an RNA training set as well as how to perform the assay in plate reader format. Furthermore, we provide details on how to perform and interpret the statistical analysis and indicate possible future extensions of the technique. By combining insights into characteristics of the small molecules and of the RNA that leads to differentiation, PRRSM promises to accelerate the elucidation of the determinants at the basis of RNA recognition.

Duke Scholars

DOI

Publication Date

January 2019

Volume

623

Start / End Page

101 / 130

Related Subject Headings

  • Spectrometry, Fluorescence
  • Software
  • Small Molecule Libraries
  • RNA
  • Principal Component Analysis
  • Organophosphorus Compounds
  • Nucleotide Motifs
  • Nucleic Acid Conformation
  • Guanidine
  • Fluorescent Dyes
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Padroni, G., Eubanks, C. S., & Hargrove, A. E. (2019). Differentiation and classification of RNA motifs using small molecule-based pattern recognition. (Vol. 623, pp. 101–130). https://doi.org/10.1016/bs.mie.2019.05.022
Padroni, Giacomo, Christopher S. Eubanks, and Amanda E. Hargrove. “Differentiation and classification of RNA motifs using small molecule-based pattern recognition.,” 623:101–30, 2019. https://doi.org/10.1016/bs.mie.2019.05.022.
Padroni, Giacomo, et al. Differentiation and classification of RNA motifs using small molecule-based pattern recognition. Vol. 623, 2019, pp. 101–30. Epmc, doi:10.1016/bs.mie.2019.05.022.

DOI

Publication Date

January 2019

Volume

623

Start / End Page

101 / 130

Related Subject Headings

  • Spectrometry, Fluorescence
  • Software
  • Small Molecule Libraries
  • RNA
  • Principal Component Analysis
  • Organophosphorus Compounds
  • Nucleotide Motifs
  • Nucleic Acid Conformation
  • Guanidine
  • Fluorescent Dyes